
Ravi Thange
ID::IN9240186


import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

import pandas as pd
df=pd.read_csv("C:\\Users\\Prime\\Pictures\\EV.csv")

df


df.head()


df.tail()


df.describe()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 112634 entries, 0 to 112633
Data columns (total 17 columns):
 #   Column                                             Non-Null Count   Dtype  
---  ------                                             --------------   -----  
 0   VIN (1-10)                                         112634 non-null  object 
 1   County                                             112634 non-null  object 
 2   City                                               112634 non-null  object 
 3   State                                              112634 non-null  object 
 4   Postal Code                                        112634 non-null  int64  
 5   Model Year                                         112634 non-null  int64  
 6   Make                                               112634 non-null  object 
 7   Model                                              112614 non-null  object 
 8   Electric Vehicle Type                              112634 non-null  object 
 9   Clean Alternative Fuel Vehicle (CAFV) Eligibility  112634 non-null  object 
 10  Electric Range                                     112634 non-null  int64  
 11  Base MSRP                                          112634 non-null  int64  
 12  Legislative District                               112348 non-null  float64
 13  DOL Vehicle ID                                     112634 non-null  int64  
 14  Vehicle Location                                   112610 non-null  object 
 15  Electric Utility                                   112191 non-null  object 
 16  2020 Census Tract                                  112634 non-null  int64  
dtypes: float64(1), int64(6), object(10)
memory usage: 14.6+ MB


df.shape

(112634, 17)


df.columns

Index(['VIN (1-10)', 'County', 'City', 'State', 'Postal Code', 'Model Year',
       'Make', 'Model', 'Electric Vehicle Type',
       'Clean Alternative Fuel Vehicle (CAFV) Eligibility', 'Electric Range',
       'Base MSRP', 'Legislative District', 'DOL Vehicle ID',
       'Vehicle Location', 'Electric Utility', '2020 Census Tract'],
      dtype='object')


df.columns = df.columns.str.replace(' ', '_')
df.columns

Index(['VIN_(1-10)', 'County', 'City', 'State', 'Postal_Code', 'Model_Year',
       'Make', 'Model', 'Electric_Vehicle_Type',
       'Clean_Alternative_Fuel_Vehicle_(CAFV)_Eligibility', 'Electric_Range',
       'Base_MSRP', 'Legislative_District', 'DOL_Vehicle_ID',
       'Vehicle_Location', 'Electric_Utility', '2020_Census_Tract'],
      dtype='object')


df.rename(columns={'Clean_Alternative_Fuel_Vehicle_(CAFV)_Eligibility':'CAFV_Eligibility'}, inplace=True)
df.columns

Index(['VIN_(1-10)', 'County', 'City', 'State', 'Postal_Code', 'Model_Year',
       'Make', 'Model', 'Electric_Vehicle_Type', 'CAFV_Eligibility',
       'Electric_Range', 'Base_MSRP', 'Legislative_District', 'DOL_Vehicle_ID',
       'Vehicle_Location', 'Electric_Utility', '2020_Census_Tract'],
      dtype='object')

df


print(df.isnull().sum())

VIN_(1-10)                 0
County                     0
City                       0
State                      0
Postal_Code                0
Model_Year                 0
Make                       0
Model                     20
Electric_Vehicle_Type      0
CAFV_Eligibility           0
Electric_Range             0
Base_MSRP                  0
Legislative_District     286
DOL_Vehicle_ID             0
Vehicle_Location          24
Electric_Utility         443
2020_Census_Tract          0
dtype: int64


df_dropna = df.dropna(inplace=True)


print(df.isnull().sum())

VIN_(1-10)               0
County                   0
City                     0
State                    0
Postal_Code              0
Model_Year               0
Make                     0
Model                    0
Electric_Vehicle_Type    0
CAFV_Eligibility         0
Electric_Range           0
Base_MSRP                0
Legislative_District     0
DOL_Vehicle_ID           0
Vehicle_Location         0
Electric_Utility         0
2020_Census_Tract        0
dtype: int64


numerical_columns = ['Postal_Code', 'Model_Year', 'Electric_Range', 'Base_MSRP', 'Legislative_District', 'DOL_Vehicle_ID', '2020_Census_Tract']


categorical_columns = ['VIN_(1-10)', 'County', 'City', 'State', 'Make', 'Model', 'Electric_Vehicle_Type', 'CAFV_Eligibility', 'Vehicle_Location', 'Electric_Utility']

discrete_df = df.select_dtypes(include=['object'])
numerical_df = df.select_dtypes(include=['int64', 'float64'])


def discrete_univariate_analysis(discrete_data):
    for col_name in discrete_data:
        print("-"*10, col_name, "-"*10)
        print(discrete_data[col_name].agg(['count', 'nunique', 'unique']))
        print('Value Counts: \n', discrete_data[col_name].value_counts())
        print()


discrete_univariate_analysis(discrete_df)

---------- VIN_(1-10) ----------
count                                                 112152
nunique                                                 7522
unique     [JN1AZ0CP8B, 1G1FW6S08H, 3FA6P0SU1K, 5YJ3E1EB5...
Name: VIN_(1-10), dtype: object
Value Counts: 
 5YJYGDEE9M    471
5YJYGDEE0M    463
5YJYGDEE7M    447
5YJYGDEE8M    446
5YJYGDEE2M    435
             ... 
YV4BR0DL8M      1
JTJHKCFZ5N      1
WA1J2BFZ3N      1
KNDC4DLC5P      1
WA1LAAGE5M      1
Name: VIN_(1-10), Length: 7522, dtype: int64

---------- County ----------
count                                                 112152
nunique                                                   39
unique     [Yakima, Skagit, Snohomish, Island, Thurston, ...
Name: County, dtype: object
Value Counts: 
 King            58980
Snohomish       12412
Pierce           8525
Clark            6681
Thurston         4109
Kitsap           3828
Whatcom          2839
Spokane          2785
Benton           1376
Island           1298
Skagit           1228
Clallam           728
San Juan          717
Jefferson         698
Chelan            654
Yakima            617
Cowlitz           569
Mason             547
Lewis             431
Grays Harbor      402
Kittitas          392
Franklin          365
Grant             335
Walla Walla       312
Douglas           221
Whitman           177
Klickitat         175
Okanogan          149
Pacific           145
Skamania          139
Stevens            91
Asotin             48
Wahkiakum          39
Adams              34
Pend Oreille       32
Lincoln            30
Ferry              27
Columbia           13
Garfield            4
Name: County, dtype: int64

---------- City ----------
count                                                 112152
nunique                                                  435
unique     [Yakima, Concrete, Everett, Bothell, Mukilteo,...
Name: City, dtype: object
Value Counts: 
 Seattle           20295
Bellevue           5919
Redmond            4199
Vancouver          4013
Kirkland           3598
                  ...  
Walla Walla Co        1
Clallam Bay           1
Malott                1
Rockport              1
Uniontown             1
Name: City, Length: 435, dtype: int64

---------- State ----------
count      112152
nunique         1
unique       [WA]
Name: State, dtype: object
Value Counts: 
 WA    112152
Name: State, dtype: int64

---------- Make ----------
count                                                 112152
nunique                                                   34
unique     [NISSAN, CHEVROLET, FORD, TESLA, KIA, AUDI, BM...
Name: Make, dtype: object
Value Counts: 
 TESLA             51883
NISSAN            12846
CHEVROLET         10140
FORD               5780
BMW                4660
KIA                4469
TOYOTA             4368
VOLKSWAGEN         2507
AUDI               2320
VOLVO              2256
CHRYSLER           1780
HYUNDAI            1407
JEEP               1143
RIVIAN              883
FIAT                820
PORSCHE             817
HONDA               788
MINI                631
MITSUBISHI          585
POLESTAR            557
MERCEDES-BENZ       503
SMART               271
JAGUAR              218
LINCOLN             167
CADILLAC            108
LUCID MOTORS         65
SUBARU               59
LAND ROVER           38
LEXUS                33
FISKER               19
GENESIS              18
AZURE DYNAMICS        7
TH!NK                 3
BENTLEY               3
Name: Make, dtype: int64

---------- Model ----------
count                                                 112152
nunique                                                  114
unique     [LEAF, BOLT EV, FUSION, MODEL 3, SOUL, Q5 E, M...
Name: Model, dtype: object
Value Counts: 
 MODEL 3        23042
MODEL Y        17086
LEAF           12846
MODEL S         7346
BOLT EV         4895
               ...  
745LE              2
S-10 PICKUP        1
SOLTERRA           1
918                1
FLYING SPUR        1
Name: Model, Length: 114, dtype: int64

---------- Electric_Vehicle_Type ----------
count                                                 112152
nunique                                                    2
unique     [Battery Electric Vehicle (BEV), Plug-in Hybri...
Name: Electric_Vehicle_Type, dtype: object
Value Counts: 
 Battery Electric Vehicle (BEV)            85732
Plug-in Hybrid Electric Vehicle (PHEV)    26420
Name: Electric_Vehicle_Type, dtype: int64

---------- CAFV_Eligibility ----------
count                                                 112152
nunique                                                    3
unique     [Clean Alternative Fuel Vehicle Eligible, Not ...
Name: CAFV_Eligibility, dtype: object
Value Counts: 
 Clean Alternative Fuel Vehicle Eligible                         58395
Eligibility unknown as battery range has not been researched    39097
Not eligible due to low battery range                           14660
Name: CAFV_Eligibility, dtype: int64

---------- Vehicle_Location ----------
count                                                 112152
nunique                                                  516
unique     [POINT (-120.50721 46.60448), POINT (-121.7515...
Name: Vehicle_Location, dtype: object
Value Counts: 
 POINT (-122.13158 47.67858)    2914
POINT (-122.2066 47.67887)     2059
POINT (-122.1872 47.61001)     2001
POINT (-122.31765 47.70013)    1878
POINT (-122.12096 47.55584)    1851
                               ... 
POINT (-121.59274 48.48758)       1
POINT (27.25316 67.01865)         1
POINT (-124.16705 47.11487)       1
POINT (-123.00026 48.61989)       1
POINT (-117.08742 46.53906)       1
Name: Vehicle_Location, Length: 516, dtype: int64

---------- Electric_Utility ----------
count                                                 112152
nunique                                                   73
unique     [PACIFICORP, PUGET SOUND ENERGY INC, PUD NO 2 ...
Name: Electric_Utility, dtype: object
Value Counts: 
 PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)                                               40231
PUGET SOUND ENERGY INC                                                                      22166
CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)                                                21439
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLARK COUNTY - (WA)                             6522
BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY              5049
                                                                                            ...  
BONNEVILLE POWER ADMINISTRATION||PENINSULA LIGHT COMPANY                                        1
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF ASOTIN COUNTY                                      1
CITY OF SEATTLE - (WA)                                                                          1
BONNEVILLE POWER ADMINISTRATION||NESPELEM VALLEY ELEC COOP, INC                                 1
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLALLAM COUNTY|PUD NO 1 OF JEFFERSON COUNTY        1
Name: Electric_Utility, Length: 73, dtype: int64


def numerical_univariate_analysis(numerical_data):
    for col_name in numerical_data:
        print("-"*10, col_name, "-"*10)
        print(numerical_data[col_name].agg(['min', 'max', 'mean', 'median', 'std']))
        print()


numerical_univariate_analysis(numerical_df)

---------- Postal_Code ----------
min       98001.000000
max       99403.000000
mean      98258.856659
median    98121.000000
std         302.889935
Name: Postal_Code, dtype: float64

---------- Model_Year ----------
min       1997.000000
max       2023.000000
mean      2019.004494
median    2020.000000
std          2.891859
Name: Model_Year, dtype: float64

---------- Electric_Range ----------
min         0.000000
max       337.000000
mean       87.829651
median     32.000000
std       102.336645
Name: Electric_Range, dtype: float64

---------- Base_MSRP ----------
min            0.000000
max       845000.000000
mean        1793.882320
median         0.000000
std        10785.259118
Name: Base_MSRP, dtype: float64

---------- Legislative_District ----------
min        1.000000
max       49.000000
mean      29.817703
median    34.000000
std       14.698726
Name: Legislative_District, dtype: float64

---------- DOL_Vehicle_ID ----------
min       4.777000e+03
max       4.792548e+08
mean      1.994712e+08
median    1.923916e+08
std       9.401842e+07
Name: DOL_Vehicle_ID, dtype: float64

---------- 2020_Census_Tract ----------
min       5.300195e+10
max       5.307794e+10
mean      5.303958e+10
median    5.303303e+10
std       1.617788e+07
Name: 2020_Census_Tract, dtype: float64


sns.set(style="whitegrid")# Univariate Analysis: Distribution of Numerical Columns

# Plot histograms for numerical columns

for column in numerical_columns:
    plt.figure(figsize=(15, 10))
    
    sns.histplot(df[column], kde=True)
    plt.title(f'Distribution of {column}')
plt.tight_layout()
plt.show()


for column in numerical_columns:
    plt.figure(figsize=(15, 10))
    
    sns.boxplot(x=df[column])
    plt.title(f'Box Plot of {column}')
plt.tight_layout()
plt.show()


def describe_outliers(df, column):
    Q1 = df[column].quantile(0.25)
    Q3 = df[column].quantile(0.75)
    IQR = Q3 - Q1
    lower_bound = Q1 - 1.5 * IQR
    upper_bound = Q3 + 1.5 * IQR
    outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]
    print(f"\
Column: {column}")
    print(f"Number of outliers: {len(outliers)}")
    print(f"Percentage of outliers: {len(outliers) / len(df) * 100:.2f}%")
    print(f"Range of outliers: {outliers[column].min()} to {outliers[column].max()}")
    print(f"Range of non-outliers: {df[(df[column] >= lower_bound) & (df[column] <= upper_bound)][column].min()} to {df[(df[column] >= lower_bound) & (df[column] <= upper_bound)][column].max()}")

for column in numerical_columns:
    describe_outliers(df, column)

Column: Postal_Code
Number of outliers: 6514
Percentage of outliers: 5.81%
Range of outliers: 98848 to 99403
Range of non-outliers: 98001 to 98847
Column: Model_Year
Number of outliers: 40
Percentage of outliers: 0.04%
Range of outliers: 1997 to 2008
Range of non-outliers: 2010 to 2023
Column: Electric_Range
Number of outliers: 0
Percentage of outliers: 0.00%
Range of outliers: nan to nan
Range of non-outliers: 0 to 337
Column: Base_MSRP
Number of outliers: 3498
Percentage of outliers: 3.12%
Range of outliers: 31950 to 845000
Range of non-outliers: 0 to 0
Column: Legislative_District
Number of outliers: 0
Percentage of outliers: 0.00%
Range of outliers: nan to nan
Range of non-outliers: 1.0 to 49.0
Column: DOL_Vehicle_ID
Number of outliers: 15483
Percentage of outliers: 13.81%
Range of outliers: 4777 to 479254772
Range of non-outliers: 100021575 to 325344519
Column: 2020_Census_Tract
Number of outliers: 34
Percentage of outliers: 0.03%
Range of outliers: 53001950100 to 53001950500
Range of non-outliers: 53003960100 to 53077940007


plt.figure(figsize=(15, 10))
for i, column in enumerate(categorical_columns[:6], 1):  # Limiting to first 6 for clarity
    plt.subplot(3, 2, i)
    sns.countplot(y=df[column], order=df[column].value_counts().index[:10])
    plt.title(f'Top 10 {column}')
plt.tight_layout()
plt.show()


# 1. Relationship between Model Year and Electric Range
plt.figure(figsize=(12, 6))
sns.scatterplot(x='Model_Year', y='Electric_Range', data=df)
plt.title('Model Year vs Electric Range')
plt.show()

# 2. Comparison of Electric Range across different Electric Vehicle Types
plt.figure(figsize=(12, 6))
sns.boxplot(x='Electric_Vehicle_Type', y='Electric_Range', data=df)
plt.title('Electric Range by Vehicle Type')
plt.xticks(rotation=45)
plt.show()

# 3. Correlation between Electric Range and Base MSRP
# First, let's check if Base MSRP has non-zero values
if df['Base_MSRP'].sum() > 0:
    plt.figure(figsize=(12, 6))
    sns.scatterplot(x='Base_MSRP', y='Electric_Range', data=df)
    plt.title('Base MSRP vs Electric Range')
    plt.show()
else:
    print("Base MSRP column contains only zero values. Skipping this analysis.")

# 4. Distribution of Electric Vehicle Types across different States
vehicle_type_by_state = df.groupby('State')['Electric_Vehicle_Type'].value_counts().unstack()
plt.figure(figsize=(15, 8))
vehicle_type_by_state.plot(kind='bar', stacked=True)
plt.title('Distribution of Electric Vehicle Types across States')
plt.xlabel('State')
plt.ylabel('Count')
plt.legend(title='Electric Vehicle Type', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.show()

<Figure size 1500x800 with 0 Axes>


# 5. Correlation matrix for numerical variables
plt.figure(figsize=(10, 8))
correlation_matrix = df.corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f')
plt.title('Correlation Matrix of Numerical Features')
plt.show()

# 6. Distribution of Electric Vehicle Types by Make
plt.figure(figsize=(14, 7))
sns.countplot(y='Make', hue='Electric_Vehicle_Type', data=df, order=df['Make'].value_counts().index)
plt.title('Distribution of Electric Vehicle Types by Make')
plt.xlabel('Count')
plt.ylabel('Make')
plt.legend(title='Electric Vehicle Type')
plt.show()

C:\Users\Prime\AppData\Local\Temp\ipykernel_11660\3143839223.py:3: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  correlation_matrix = df.corr()


# Assuming 'df' is your DataFrame
df.boxplot(by="CAFV_Eligibility", column=['Electric_Range'])

# Rotate x-axis labels by 90 degrees
plt.xticks(rotation=90)

# Show the plot
plt.show()


import plotly.express as px


ev_count_by_state = df.groupby('State').size().reset_index(name='Number_of_EV_Vehicles')
ev_count_by_state


# Count the number of EVs per state
ev_count_by_state = df['State'].value_counts().reset_index()
ev_count_by_state.columns = ['State', 'EV_Count']

# Create the Choropleth map
fig = px.choropleth(ev_count_by_state,
                    locations='State', 
                    locationmode="USA-states", 
                    color='EV_Count',
                    scope="usa",
                    color_continuous_scale="Viridis",
                    title="Number of Electric Vehicles by State")

# Update the layout
fig.update_layout(
    title_x=0.5,
    geo_scope='usa',
)

fig.show()

# Save the plot as an HTML file
fig.write_html("ev_choropleth_map.html")

print("Choropleth map has been created and saved as 'ev_choropleth_map.html'.")
print("\
Top 5 states by EV count:")
print(ev_count_by_state.head().to_string(index=False))

Choropleth map has been created and saved as 'ev_choropleth_map.html'.
Top 5 states by EV count:
State  EV_Count
   WA    112152


import pandas as pd
import plotly.express as px

# Load the dataset
df = pd.read_csv('C:\\Users\\Prime\\Pictures\\EV.csv', encoding='ascii')

# Count the number of EVs per postal code
ev_count_by_postal = df['Postal Code'].value_counts().reset_index()
ev_count_by_postal.columns = ['Postal Code', 'EV_Count']

# Merge the count with the original dataframe to get location data
df_merged = df.merge(ev_count_by_postal, on='Postal Code')

# Extract latitude and longitude from the 'Vehicle Location' column
df_merged['Longitude'] = df_merged['Vehicle Location'].str.extract('POINT \(([-\d.]+) ')
df_merged['Latitude'] = df_merged['Vehicle Location'].str.extract(' ([-\d.]+)\)')

# Convert to numeric
df_merged['Longitude'] = pd.to_numeric(df_merged['Longitude'])
df_merged['Latitude'] = pd.to_numeric(df_merged['Latitude'])

# Create the scatter plot on a map
fig = px.scatter_mapbox(df_merged, 
                        lat='Latitude', 
                        lon='Longitude', 
                        color='EV_Count',
                        size='EV_Count',
                        hover_name='Postal Code',
                        hover_data=['City', 'State', 'EV_Count'],
                        color_continuous_scale="Viridis",
                        size_max=15,
                        zoom=3,
                        title="Number of Electric Vehicles by Postal Code")

fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})

# Save the plot as an HTML file
fig.write_html("ev_postal_code_map.html")

fig.show()

print("Scatter map based on postal codes has been created and saved as 'ev_postal_code_map.html'.")
print("\
Top 10 postal codes by EV count:")
print(ev_count_by_postal.head(10).to_string(index=False))

# Display some statistics
print("\
Total number of unique postal codes:", len(ev_count_by_postal))
print("Average number of EVs per postal code:", round(ev_count_by_postal['EV_Count'].mean(), 2))
print("Median number of EVs per postal code:", ev_count_by_postal['EV_Count'].median())
print("Maximum number of EVs in a single postal code:", ev_count_by_postal['EV_Count'].max())

Scatter map based on postal codes has been created and saved as 'ev_postal_code_map.html'.
Top 10 postal codes by EV count:
 Postal Code  EV_Count
       98052      2916
       98033      2059
       98004      2001
       98115      1880
       98006      1852
       98012      1850
       98072      1661
       98040      1639
       98074      1594
       98034      1578
Total number of unique postal codes: 773
Average number of EVs per postal code: 145.71
Median number of EVs per postal code: 7.0
Maximum number of EVs in a single postal code: 2916

Requirement already satisfied: bar-chart-race in c:\users\prime\anaconda3\lib\site-packages (0.1.0)
Requirement already satisfied: pandas>=0.24 in c:\users\prime\anaconda3\lib\site-packages (from bar-chart-race) (1.5.3)
Requirement already satisfied: matplotlib>=3.1 in c:\users\prime\anaconda3\lib\site-packages (from bar-chart-race) (3.7.1)
Requirement already satisfied: contourpy>=1.0.1 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar-chart-race) (1.0.5)
Requirement already satisfied: cycler>=0.10 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar-chart-race) (0.11.0)
Requirement already satisfied: fonttools>=4.22.0 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar-chart-race) (4.25.0)
Requirement already satisfied: kiwisolver>=1.0.1 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar-chart-race) (1.4.4)
Requirement already satisfied: numpy>=1.20 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar-chart-race) (1.24.3)
Requirement already satisfied: packaging>=20.0 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar-chart-race) (23.0)
Requirement already satisfied: pillow>=6.2.0 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar-chart-race) (9.4.0)
Requirement already satisfied: pyparsing>=2.3.1 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar-chart-race) (3.0.9)
Requirement already satisfied: python-dateutil>=2.7 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar-chart-race) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in c:\users\prime\anaconda3\lib\site-packages (from pandas>=0.24->bar-chart-race) (2022.7)
Requirement already satisfied: six>=1.5 in c:\users\prime\anaconda3\lib\site-packages (from python-dateutil>=2.7->matplotlib>=3.1->bar-chart-race) (1.16.0)


!pip install bar_chart_race

Requirement already satisfied: bar_chart_race in c:\users\prime\anaconda3\lib\site-packages (0.1.0)
Requirement already satisfied: pandas>=0.24 in c:\users\prime\anaconda3\lib\site-packages (from bar_chart_race) (1.5.3)
Requirement already satisfied: matplotlib>=3.1 in c:\users\prime\anaconda3\lib\site-packages (from bar_chart_race) (3.7.1)
Requirement already satisfied: contourpy>=1.0.1 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar_chart_race) (1.0.5)
Requirement already satisfied: cycler>=0.10 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar_chart_race) (0.11.0)
Requirement already satisfied: fonttools>=4.22.0 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar_chart_race) (4.25.0)
Requirement already satisfied: kiwisolver>=1.0.1 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar_chart_race) (1.4.4)
Requirement already satisfied: numpy>=1.20 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar_chart_race) (1.24.3)
Requirement already satisfied: packaging>=20.0 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar_chart_race) (23.0)
Requirement already satisfied: pillow>=6.2.0 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar_chart_race) (9.4.0)
Requirement already satisfied: pyparsing>=2.3.1 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar_chart_race) (3.0.9)
Requirement already satisfied: python-dateutil>=2.7 in c:\users\prime\anaconda3\lib\site-packages (from matplotlib>=3.1->bar_chart_race) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in c:\users\prime\anaconda3\lib\site-packages (from pandas>=0.24->bar_chart_race) (2022.7)
Requirement already satisfied: six>=1.5 in c:\users\prime\anaconda3\lib\site-packages (from python-dateutil>=2.7->matplotlib>=3.1->bar_chart_race) (1.16.0)


import bar_chart_race as bcr
import warnings


df['Model Year'] = df['Model Year'].astype(str)

# Group the data by 'Model Year' and 'Make', then count the occurrences
grouped_data = df.groupby(['Model Year', 'Make']).size().reset_index(name='Count')

# Pivot the data to have 'Model Year' as the index and 'Make' as columns
pivoted_data = grouped_data.pivot(index='Model Year', columns='Make', values='Count')

# Fill missing values with 0 (for years where some makes might have no entries)
pivoted_data = pivoted_data.fillna(0)

# Create the bar chart race animation and save it as a GIF
bcr.bar_chart_race(df=pivoted_data, filename='EV_racing_bar_plot.gif',
                   orientation='h', sort='desc', n_bars=10,
                   title='EV Make Count Over the Years', filter_column_colors=True, period_length=1000)

C:\Users\Prime\anaconda3\Lib\site-packages\bar_chart_race\_make_chart.py:286: UserWarning:

FixedFormatter should only be used together with FixedLocator

C:\Users\Prime\anaconda3\Lib\site-packages\bar_chart_race\_make_chart.py:287: UserWarning:

FixedFormatter should only be used together with FixedLocator

MovieWriter imagemagick unavailable; using Pillow instead.

	Postal Code	Model Year	Electric Range	Base MSRP	Legislative District	DOL Vehicle ID	2020 Census Tract
count	112634.000000	112634.000000	112634.000000	112634.000000	112348.000000	1.126340e+05	1.126340e+05
mean	98156.226850	2019.003365	87.812987	1793.439681	29.805604	1.994567e+08	5.296650e+10
std	2648.733064	2.892364	102.334216	10783.753486	14.700545	9.398427e+07	1.699104e+09
min	1730.000000	1997.000000	0.000000	0.000000	1.000000	4.777000e+03	1.101001e+09
25%	98052.000000	2017.000000	0.000000	0.000000	18.000000	1.484142e+08	5.303301e+10
50%	98119.000000	2020.000000	32.000000	0.000000	34.000000	1.923896e+08	5.303303e+10
75%	98370.000000	2022.000000	208.000000	0.000000	43.000000	2.191899e+08	5.305307e+10
max	99701.000000	2023.000000	337.000000	845000.000000	49.000000	4.792548e+08	5.603300e+10

¶

task 1¶

Visual Univariate Analysis on Numerical Columns¶

Outlier Detection¶

Visual Univariate Analysis on Categorical Variables¶

Bivariate Analysis¶

Task 2: Create a Choropleth using plotly.express to display the number of EV vehicles based on location¶

Task 3: Create a Racing Bar Plot to display the animation of EV Make and its count each year.v¶

	VIN (1-10)	County	City	State	Postal Code	Model Year	Make	Model	Electric Vehicle Type	Clean Alternative Fuel Vehicle (CAFV) Eligibility	Electric Range	Base MSRP	Legislative District	DOL Vehicle ID	Vehicle Location	Electric Utility	2020 Census Tract
0	JTMEB3FV6N	Monroe	Key West	FL	33040	2022	TOYOTA	RAV4 PRIME	Plug-in Hybrid Electric Vehicle (PHEV)	Clean Alternative Fuel Vehicle Eligible	42	0	NaN	198968248	POINT (-81.80023 24.5545)	NaN	12087972100
1	1G1RD6E45D	Clark	Laughlin	NV	89029	2013	CHEVROLET	VOLT	Plug-in Hybrid Electric Vehicle (PHEV)	Clean Alternative Fuel Vehicle Eligible	38	0	NaN	5204412	POINT (-114.57245 35.16815)	NaN	32003005702
2	JN1AZ0CP8B	Yakima	Yakima	WA	98901	2011	NISSAN	LEAF	Battery Electric Vehicle (BEV)	Clean Alternative Fuel Vehicle Eligible	73	0	15.0	218972519	POINT (-120.50721 46.60448)	PACIFICORP	53077001602
3	1G1FW6S08H	Skagit	Concrete	WA	98237	2017	CHEVROLET	BOLT EV	Battery Electric Vehicle (BEV)	Clean Alternative Fuel Vehicle Eligible	238	0	39.0	186750406	POINT (-121.7515 48.53892)	PUGET SOUND ENERGY INC	53057951101
4	3FA6P0SU1K	Snohomish	Everett	WA	98201	2019	FORD	FUSION	Plug-in Hybrid Electric Vehicle (PHEV)	Not eligible due to low battery range	26	0	38.0	2006714	POINT (-122.20596 47.97659)	PUGET SOUND ENERGY INC	53061041500
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
112629	7SAYGDEF2N	King	Duvall	WA	98019	2022	TESLA	MODEL Y	Battery Electric Vehicle (BEV)	Eligibility unknown as battery range has not b...	0	0	45.0	217955265	POINT (-121.98609 47.74068)	PUGET SOUND ENERGY INC\|\|CITY OF TACOMA - (WA)	53033032401
112630	1N4BZ1CP7K	San Juan	Friday Harbor	WA	98250	2019	NISSAN	LEAF	Battery Electric Vehicle (BEV)	Clean Alternative Fuel Vehicle Eligible	150	0	40.0	103663227	POINT (-123.01648 48.53448)	BONNEVILLE POWER ADMINISTRATION\|\|ORCAS POWER &...	53055960301
112631	1FMCU0KZ4N	King	Vashon	WA	98070	2022	FORD	ESCAPE	Plug-in Hybrid Electric Vehicle (PHEV)	Clean Alternative Fuel Vehicle Eligible	38	0	34.0	193878387	POINT (-122.4573 47.44929)	PUGET SOUND ENERGY INC\|\|CITY OF TACOMA - (WA)	53033027702
112632	KNDCD3LD4J	King	Covington	WA	98042	2018	KIA	NIRO	Plug-in Hybrid Electric Vehicle (PHEV)	Not eligible due to low battery range	26	0	47.0	125039043	POINT (-122.09124 47.33778)	PUGET SOUND ENERGY INC\|\|CITY OF TACOMA - (WA)	53033032007
112633	YV4BR0CL8N	King	Covington	WA	98042	2022	VOLVO	XC90	Plug-in Hybrid Electric Vehicle (PHEV)	Not eligible due to low battery range	18	0	47.0	194673692	POINT (-122.09124 47.33778)	PUGET SOUND ENERGY INC\|\|CITY OF TACOMA - (WA)	53033032005